Method and system tracking and determining public sentiment relating to social media

ABSTRACT

A method for determining influential broadcast messages in which a processing device identifies a plurality of re-posts of a post, wherein each re-post comprises a time stamp, determines whether the time stamp of the re-post is within a first time interval or a second time interval, increments a first count associated with the first time interval when the time stamp is within the first time interval or a second count associated with the second time interval when the time stamp is within the second time interval, and determines a public sentiment attribute associated with the post in view of a comparison between the first count and the second count.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 61/670,342 filed Jul. 11, 2012, the disclosure of whichis incorporated herein by reference in its entirety.

TECHNICAL FIELD

This disclosure generally relates to analyzing content on the World WideWeb to track and determine public sentiment relating to social media.More specifically, this disclosure uses a statistical approach to trackand analyze user-submitted content (posts and re-posts) on the Web andto determine the respective public sentiment of that content.

BACKGROUND

The World Wide Web or Internet is a global resource with an abundance ofcontent and information created by people across the globe, hereinreferred to as “users”. User submitted content can be found on severaldifferent social media platforms, Web sites, social media networks, andother Web sources that are publicly available on the Web. Examples ofparticular social media platforms, which provide a mechanism for usersto submit content, include Twitter™, Facebook™, and Pinterest™, whichare examples of micro-blog, content, and image sharing platforms. Usersof such social media platforms can post a message, micro-blog message,or image, herein referred to as a “post” for others to read, view,respond to, and share. For example, the Twitter™ social media platformcurrently supports more than 100 million users and over 400 millionposts (i.e., Tweets) per day. With such social media platformscontaining such a vast amount of user-generated content, data from asocial media platform can be a valuable resource for determining publicsentiment relating to various issues, topics, people, or companies.

Many people are interested in analyzing such social media contentavailable on the Web, particularly content created by user activity onsocial media sites, such as, for example, Twitter™, Facebook™, andPinterest™. One particular example of a set of users who may beinterested in such analysis is stock market traders, who generally havean interest in the public sentiment of companies or other financiallyimpactful information. Many solutions are currently available to monitoronline content, such as basic Web aggregation software, but thesesolutions fail to measure the real time public sentiment of social mediausers. For example, a search can be performed using key words thatanalysts are interested in, creating a simple count of key word mentionson social media platforms. The amount of mentions can provide a basicmeasurement of interest, and a large number of key word mentions candetermine a high interest in a topic, but there is little context forthis amount in regards to time or rate of increase. While this knownapproach has been conducted in prior art and is commonly used today,modern social media sites provide more mechanisms for sharinginformation. Social media sites often provide a mechanism to re-postcontent from users, or to copy and republish the same content thatinterests them. For example, Twitter™ provides a mechanism to re-postuser-submitted content through a mechanism coined as a “Retweet”. ARetweet is created by a Tweet that has been posted on the social mediaplatform Twitter™. The Tweet can then be read by another Twitter™ user,who can then re-post the Tweet based on the user's interest in theTweet. In other words, the same short message that has been posted by auser can be re-posted by a different user, which is called a Retweet.Retweets can continue to be re-posted by additional users to create anexpanding content base of Retweets of the same message.

In another example, Facebook™ provides a mechanism to re-post theuser-submitted content through a mechanism coined a “Like”. A Likecreates a connection between the posted content on the social networkingsite Facebook™ and a different user who likes or affirms a person'sposted content. In another example, Pinterest™ provides a mechanism tore-post the user-submitted content through a mechanism coined a“Re-pin”. Users can browse others posts for images and “Re-pin” imagesto their own account.

Simply mentioning a specific area of interest does not providesufficient information to fully articulate public perception of a topic.For example, a user can mention a certain word such as “Factories” in amessage, and several users can mention the same word “Factories”, butcounting how many times the word factories is mentioned will not provideinformation greater than the information present at hand (e.g.,factories mentioned X number of times).

Accordingly, what would be desirable is a method that can determine thepublic sentiment attribute of a post. Moreover, due to the widespreaduse of social networking sites such as, for example, Twitter™,Facebook™, and Pinterest™, the public sentiment of issues, a company, orany other area of interests may be measured using the methods describedherein.

While simply counting the number of posts containing significant keywords would provide some measure of public sentiment, what would bedesirable is a method that provides a more complex measure of the growthand expansion of re-posts of a message or an image.

SUMMARY

Presented herein is a method to track and determine a public sentimentattribute relating to social media, to neglect irrelevant data, and tocreate a method for measuring an expansion rate of re-posts to determinea public sentiment about an original post.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure may be more readily understood from the detaileddescription of an exemplary embodiment presented below considered inconjunction with the attached drawings and in which like referencenumerals refer to similar elements, and in which:

FIG. 1A is a block diagram of one embodiment of an environment fortracking and determining a public sentiment attribute relating to asocial media post;

FIG. 1B is a block diagram of one embodiment of a subsystem for trackingand determining a public sentiment attribute relating to a social mediapost;

FIG. 2 is a flow diagram illustrating one embodiment of a method fortracking and determining a public sentiment attribute relating to asocial media post;

FIG. 3 is an example of a data table illustrating one embodiment of amethod for tracking and determining a public sentiment attributerelating to a social media post; and

FIG. 4 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system within which a set of instructionsor processing logic may be executed for causing the machine to performany one or more of the methodologies discussed herein.

It is to be understood that the attached drawings are for purposes ofillustrating the concepts of the disclosure and may not be to scale.

DETAILED DESCRIPTION

The present disclosure provides a method and system for individualclient or server machines to collect user-generated Web content and tocalculate public perception of a post based on re-posts. Using theaggregated Web content from social media platforms, for example,Twitter™ and its Retweeting feature, or Facebook™ and its Like feature,or Pinterest™ and its Re-pin feature, the present disclosure describesan algorithm further described herein that calculates the rate ofexpansion of re-posted messages (e.g., Retweets, Likes, or Re-pins,etc.). A re-post expansion rate is measured to determine thesignificance of the subject matter and public interest in the originalpost. In an example, the disclosure includes a method which may bereferred to as the Velocity Of ReTweet Expansion, or VORTEX.

As used herein, the term “post” is intended to include any typecommunication including a short entry text message, an audio message, amessage content, a broadcast message, an image, a video, or combinationthereof that may be displayed by a user on a social media account (e.g.,a Tweet on the Twitter™ platform, a pin on the Pinterest™ platform, astatus update, comment, photo, audio playback, or advertisement on theFacebook™ platform, and the like) for other users to consume (e.g.,read, view, respond to, and/or share). As used herein, the term“re-post” is intended to include a replication or reference to anaffirmation of the post. Examples of a re-post include, but are notlimited to, a republishing of a short message that has previously beenposted (e.g., a “Retweet” on the Twitter™ platform), an affirmation of amessage (e.g., a “Like” on the Facebook™ platform), and a republishingof an image that has previously been posted (e.g., a “Re-pin” on thePinterest™ platform). It is to be understood that the above describedsocial media platforms have been made in an illustrative fashion, andnot a restrictive one, and that any post and re-post may be tracked andanalyzed.

According to an embodiment, the present disclosure can be implemented aspart of a computer program, application, computer-executableinstructions, or software package. As used herein, the term “program”,“application”, “software package”, “processing logic”, or “computerexecutable instructions” refers to instructions that may be performed bya processor and/or other suitable components. The term “computer” or“server”, as used herein, is not limited to any one particular type ofhardware device, but may be any data processing device such as a desktopcomputer, a laptop computer, a kiosk terminal, a personal digitalassistant (PDA) or any equivalents or combinations thereof. Any deviceor part of a device configured to process, manage or transmit data,whether implemented with electrical, magnetic, optical, biologicalcomponents or otherwise, may be made suitable for implementing thedisclosure described herein.

As used herein, the term “communicatively connected” is intended toinclude any type of connection, whether wired or wireless, in which datamay be communicated. Furthermore, the term “communicatively connected”is intended to include a connection between devices and/or programswithin a single computer or between devices and/or programs on separatecomputers.

FIG. 1A is a diagram of one embodiment of an environment 100 fortracking and determining a public sentiment attribute relating to asocial media post.

In one embodiment, the environment 100 includes a network 102 (e.g.,Internet, World Wide Web, etc.), which may be any type of communicationsmedium that allows for tracking and determining public sentimentrelating to social media between the user interface 101, the posts andre-posts 110, and the system and processing logic for tracking anddetermining public sentiment 120 (“the determining system 120”). In anexample, the system, or portion thereof, may be referred to as VORTEX.

The determining system 120 includes a computing device such as a serverand processing logic for tracking and determining public sentimentrelating to social media. The determining system 120 can be a desktopcomputer, a laptop computer, a dedicated server computer, a consoledevice, a network device, or a similar computing device. As used herein,“public sentiment” refers to an aggregate of individual attitudes orbeliefs about a certain topic. As used herein, “public sentimentattribute” refers to, for example, an attribute or a label that may beassociated with the post which describes a public sentiment about thepost. As used herein, “social media” refers to a means for network orInternet-based interactions among people in which they can create,share, and exchange information and ideas. In an embodiment, thedetermining system 120 can operate as a stand-alone system as shown inFIG. 1A. In another embodiment, the determining system 120 can beintegrated with the user interface 101 (not shown). In anotherembodiment, the determining system 120 can be integrated with the socialmedia platform (not shown).

The environment 100 includes a user interface 101, which may be anydevice capable of enabling a user 105 to communicate with thedetermining system 110 via the network 102, including, but not limitedto a computer, a smart phone, a wireless device, a tablet, and/or anysuitable computing device. In an embodiment, the user interface 101 maybe a mobile device (e.g., a handheld device, smart phone or web portaldevice) that communicates with the network 102 via a wirelessconnection. As used herein, the term “user” is intended to include anindividual person, a group of people, a corporate entity, or the like,who is interested in tracking and determining public sentiment relatingto social media.

The environment 100 includes a stream of posts and re-posts 110. Thestream of posts and re-posts 110 may be received or identified from afeed from the social media platform, Web aggregation, an ApplicationProgramming Interface (API), or any other method available forretrieving content over the network 102 or Internet. In an embodiment,the stream of posts and re-posts 110 can be analyzed by the determiningsystem 120.

FIG. 1B is a block diagram of one embodiment of the determining systemfor tracking and determining public sentiment relating to social media.In an embodiment, the determining system 120 may include units fortracking and a public sentiment attribute relating to a social mediapost as illustrated in FIG. 1B. The determining system 120 can include afilter unit 122, a re-post identifying unit 124, a re-post tracking unit126, a public sentiment determining unit 128, and a data store 130.

In an embodiment, the filter unit 122 may analyze the stream of postsand re-posts 110 for tracking and determining public sentiment relatingto social media. A certain key word or a combination of key words and/orunique identifiers (e.g., a company identifier) can be provided to thefilter unit 122 for capturing relevant data. The key word, thecombination of key words, and/or the company identifiers can be selectedto create a search service for a specific company or area of interest.For instance, to find information about a company like Google™, a keyword of “Google” can be used, or alternatively stock symbol “GOOG” canbe used. A list of the key words, key word combinations, and uniquecompany identifiers can be maintained by the determining system 120.When the key word, key word combinations, and/or unique companyidentifiers is mentioned in a content, a post, or a re-post in aparticular social media platform such as Twitter™, the particular socialmedia platform can be linked to this list and the post or re-post can becollected and analyzed by the determining system 120.

In an embodiment, the filter unit 122 may remove irrelevant contentbased on key word, key word combinations, and/or unique companyidentifiers. The filter unit 122 can filter incoming content (e.g.,stream of posts and re-posts 110) using a variety of string matchingtechniques, including but not limited to regular expressions or stringmatching libraries used in programming. Incoming content that matches akey word using one of these methods can be passed to the contentidentifying unit 124 for further identification. The filter unit 122 candiscard or ignore incoming content that does not match the key word.

In an embodiment, the content (stream of posts and re-posts 110) can bepassed through the filter unit to become a feed of filtered content. Thefeed at this stage may contain content that the user 105 may beinterested in based on the key word, key word combinations, and/orunique company identifiers that are used in the filter unit 122.

The re-post identifying unit 124 may be responsible for analyzing andidentifying a stream of user content or the stream of posts and re-posts110 that have been filtered by the filtering unit 122. In oneembodiment, the re-post identifying unit 124 may identify whether acertain user content is a post or a re-post. This can be identifiedbased on the content of the message. Certain words or syntax in themessage body, such as RT (Retweet), via, HT (heard through), MRT(modified Retweet), OH (overheard), PRT (partial Retweet) and other keywords, can be used to determine if the message is a re-post of anothermessage. If the re-post identifying unit 124 identifies that the messagecontent is not a re-post, the re-post identifying unit 124 can ignore ordiscard the message in the user content for the purposes of thisexample. Content which are identified to be re-posts can be passedthrough to the re-post tracking unit 126.

In an embodiment, the re-post tracking unit 126 can communicate with thedata store 130 to analyze, identify and determine if a re-post is onethat is currently being tracked for public sentiment analysis. As usedherein, the term “data store” refers to a repository for containing andstoring data. The data that is stored in data store 130 may be relatedto tracking and storing a record of re-post content, which can include atime stamp that reflects the corresponding date and time that thecontent was re-posted, a re-post count, and any other information thatmay be relevant to that re-post. As used herein, a time stamp is asequence of characters or encoded information identifying when a certainevent occurred, including the date and time of day, accurate to afraction of a second. If a re-post is not in the data store 130 fortracking, for example, if the re-post is a new content, then re-posttracking unit 126 can insert the re-post into the data store 130, alongwith the re-post date and time and any other information deemed useful.

If the re-post tracking unit 126 identifies that the re-post is alreadyin the data store 130, the public sentiment determining unit 128 cancompare the time stamp of the original post to the time stamp of there-post. If the time difference is within a time threshold or a timeinterval, a count corresponding to the time threshold or time intervalcan be incremented. For instance, if an original post has a time stampof 10:00:00, two thresholds or time intervals may be generated to trackre-posts. For example, if each threshold or time interval is 5 minutes,then two re-post intervals can be created for the time periods of10:00:00-10:04:59 (e.g., a first time interval) and 10:05:00-10:09:59(e.g., a second time interval).

In an embodiment, the beginning time of each time interval can coincidewith the time stamp of the original post. For example, if the time stampof the original post is 10:03:52, then the first time interval can runfrom 10:03:52 thru 10:08:51 and the second time interval can run from10:08:52 thru 10:13:51.

In another embodiment, the time intervals can be of varying sizes and donot need to be contiguous. For example, if the time stamp of theoriginal post is 02:05:15, then the first time interval can run from02:05:15 thru 03:05:14 and the second time interval can run from08:05:15 thru 11:05:14. In this example, different rates can be capturesfor different types of users. For instance, the first interval can betargeted for the users who may constantly be monitoring the social mediaplatform and may have a tendency to respond quickly with a re-post to apost, while the second interval can be targeted for the users who mayperiodically monitor the social media platform and may not be asresponsive with a re-post to a post.

The public sentiment determining unit 128 can analyze additionalre-posts and their corresponding time stamps and increment count in therespective time intervals based on the respective time stamp. Forinstance, if a re-post has a time stamp of 10:03:00, and the content ofthe re-post matches the content of an existing post or re-post that maybe stored in the data store 130, the time stamp of 10:03:00 can becompared against the two established time intervals. In this example,the time stamp falls within the first time interval, 10:00:00-10:04:59,so the count that is associated with this time interval is incremented.Thus, the public sentiment determining unit 128 can maintain a count ofre-posts based on their time stamps respective to the time stamp of theoriginal post.

In one embodiment, if the time stamp of the re-post does not fall withinone of the established time intervals, e.g., if the time stamp is afterthe latest interval, then the public sentiment determining unit 128 candiscard or ignore the re-post. In another embodiment, if the time stampof the re-post does not fall within one of the established timeintervals, (e.g., if the time stamp is after the expiration of thelatest interval), then the public sentiment determining unit 128 cancreate an additional time interval inclusive of the time stamp forfurther analysis.

FIG. 2 illustrates a flow diagram of one embodiment of a method fortracking and determining a public sentiment attribute relating to asocial media post. At Block 205, the data store 130 can be queried forre-posts that are currently being tracked. This query may include, butis not limited to, selecting re-post records in the data store 130 basedon a time stamp of a re-post. In an embodiment, the re-post records thathave a time interval which have elapsed or expired within a thresholdfrom the time of a current re-post can be queried. For instance, eventhough the re-post records in which the second, third, or any number oftime intervals may have elapsed or expired within a certain amount oftime (e.g., within 1 second, within 1 minute, within 1 hour, etc.)relative to the time stamp of the current re-post, the re-post recordscan still be queried. It can be envisioned that any number of timeintervals may be used for selecting records based on a number ofcriteria, resulting in a number of re-post records to be used forcomparison.

In an embodiment, the most recent time intervals which have expired canbe compared based on the current time stamp. For instance, a re-postrecord may be determined to have three time intervals, for example,10:00:00-10:04:59, 10:05:00-10:09:59, and 10:10:00-10:14:59. If the timestamp of the current re-post is 10:10:01, the determining system 120 cancompare the first and second time intervals, based on the second timeinterval just expiring. If the time stamp of the current re-post is10:15:01, the determining system 120 can compare the second and thirdintervals. In another embodiment, the determining system 120 can analyzeall intervals prior to the most recently expired interval. For instance,based on the intervals described above, if the time stamp of the currentre-post is 10:15:01, the determining system 120 may include all 3expired columns for analysis.

At Block 210, the time intervals can be checked to determine whether ornot the latest time interval has elapsed. If the latest time intervalhas elapsed, a new time interval can be created to include the timestamp of the re-post. If the latest time interval has not elapsed, adetermination can be made as to whether the time stamp of the re-post iswithin the first time interval or the second time interval. In anembodiment, the re-post count of the most recently elapsed or expiredtime interval can be compared to the re-post count of the previous timeinterval. At block 215, a count of the first time interval or a count ofthe second time interval can be incremented in view of the determinationof whether the time stamp of the re-post is within the first timeinterval or the second time interval.

At Block 220, a public sentiment attribute associated with the post orre-post in view of a comparison between the count of the first timeinterval and the count of the second time interval can be determined.For example, if the re-post count associated with the second timeinterval is greater than the re-post count associated with the firsttime interval, a public sentiment attribute can be determined to be, forexample, “growing” to indicate a growing public sentiment with respectto the original post. If the re-post count associated with the secondtime interval is less than the re-post count associated with the firsttime interval, a public sentiment attribute can be determined to be, forexample, “diminishing” or “not growing” with respect to the originalpost. In another embodiment, a threshold can be used to determinewhether to publish, or to send a message comprising the public sentimentattribute or classification to a user associated with the original post.

In an embodiment, the time intervals can be checked to determine whetheror not the latest time interval has elapsed as a stand-alone process. Inother words, the time intervals can be checked and the count of thefirst time interval and the count of the second time interval can becompared in the absence of an arrival of a re-post. In an embodiment,this check and comparison can be performed on a scheduled basis. Forexample, the check for an expired time interval and the interval countcomparison can be executed periodically (e.g., every 1 second, everyminute, every 10 minutes, every hour, etc.). In an embodiment, the checkfor the expired time interval and the interval count comparison can betriggered via a scheduler process such as a cron or a time-based jobscheduler or other task scheduler. In an embodiment, the periodic checkand comparison can be executed by a process running on a loop with aninterval for executing the check and comparison.

In an embodiment, the public sentiment attribute or classification ofthe post can be qualified by a percent increase. For instance, increasepercentage threshold of 100% can be assigned as a threshold. Forexample, if the count of re-posts in the first time interval is 10, there-post would only publish if the count of re-posts has increased by100% or more for the next time interval, or 20 re-posts in the secondtime interval. For this example, if the second time interval only has 18re-posts, re-post would not be published to the user, despite theincrease in re-posts from the first time interval to the second timeinterval.

In another embodiment, a numeric threshold may be configured for thesecond count being compared. For instance, the re-post count in eitherthe first or the second time interval (or both) may be required to begreater than a predetermined count, such as 10,000 re-posts, otherwisethe public sentiment attribute is not deemed interesting nor worthpublishing.

In another embodiment, the re-post counts of multiple time intervals maybe compared to determine if a re-post should be published. For instance,the difference in re-post count from interval 1 to interval 2 may becompared to the difference from interval 2 to interval 3. In oneembodiment, if the second difference from interval 2 to 3 is greaterthan the first difference from interval 1 to 2, it can be determinedthat interest in the re-post is continuing to grow over multipleintervals and the re-post can be published.

In another embodiment, a threshold may be used for analyzing 3 or moretime intervals. The comparisons of interval differences may be used asdescribed above, with the added requirement that the second differencemay need to be a certain percentage higher than the first difference.For instance, the re-post count for the second interval may need to beat least 50% greater than the re-post count for the first interval todetermine continued increasing interest in the post. In anotherembodiment, a numeric threshold may be used to determine a publicsentiment attribute of “increasing interest”, such as if the seconddifference increased by at least 5,000 re-posts over the firstdifference.

In another embodiment using multiple interval differences, slightdecrease in re-posts from the first to second time intervals may beallowed without affecting the public sentiment attribute. This decreasemay be based on a percentage of the first time interval or based on anumeric value. For instance, where the decrease threshold is 10%, andthe first interval is 100,000, the process may allow a second intervalgreater than 90,000 to still be deemed to have a public sentimentattribute of “interesting” and therefore worthy of publication.

After a re-post is determined to have a public sentiment attribute of“interesting” and worthy of publication, the re-post may be publishedusing any number of formats, such as, but not limited to text, XML HTMLand may be delivered to an end user or other recipient via any number oftechnologies, such as, but not limited to TCP feed, FTP, HTTP Web pages,or any other hosted solution or direct feed. Published content may alsobe forwarded to another system for further processing or analysis. In anembodiment, the above described method, or portion thereof, may bereferred to as the Velocity Of Re-post Expansion, or VORTEX.

FIG. 3 is an example of a data table 300 illustrating one embodiment ofa method for tracking and determining a public sentiment attributerelating to a social media post. The data table 300 can be stored, forexample, in data store 130 of the determining system 120. The exampledata table 300 includes a header section 302, and 5 different examplesof content analysis 304, 306, 308, 310, and 312, respectively.

The header section 302 includes six columns of data. The first column ofdata, post ID, are unique identifiers of different posts. The postcontent (column 2) includes certain key words, combination of key wordsand/or unique identifiers (e.g., a company identifier) that are presentin the respective post ID. The post content can be used by the filterunit 122, as discussed above, to filter incoming content (stream ofposts and re-posts 110) using string matching techniques. The initialpost time (column 3) is the date and time of the original post. Theinterval 1 count and interval 2 count (columns 4 and 5, respectively)reflect the number of re-posts in each respective time interval. Theclassification (column 6) can be the determined public sentimentattribute for each post ID.

For example, post ID 0 includes the key words “Company A” and “invent”.Since the number of re-posts in interval 2 is greater than that ofinterval 1, this post can be classified with a public sentimentattribute as “growing”.

Regarding post ID 1, which includes the key words “Brand B” and“computer”, the number of re-posts in interval 2 is less than that ofinterval 1. Hence, this post can be classified with a public sentimentattribute as “not interesting”. For post ID 2, having the key words“Brand C” and “automobile”, the number of re-posts in interval 2 is morethan 100% than that of interval 1. Hence this post can be classifiedwith a public sentiment attribute as “substantially growing”.

With respect to post ID 3, which includes the key words “Company D” and“IPO”, the number of re-posts in interval 2 is less than that ofinterval 1. However, since each count exceeds a threshold, this post canbe classified with a public sentiment attribute as “high interest”. Forpost ID 4, having the key words “Company E” and “Bankrupt”, the numberof re-posts in interval 2 is less than that of interval 1. Hence, thispost can be classified with a public sentiment attribute as “notinteresting”.

It is understood that the scientific data can be leveraged as anotification system, a publishing system, or other analytics based oneither the re-post expansion phenomena or the information contained inthe initial tweet since the information is now held to a greaterstandard as proven by the disclosure presented. Other information thatcan be published based on this disclosure and these findings caninclude, but are not limited to, the rate of growth of a post orre-post, the number of re-posts in a time interval, the original userand their influence on the Retweet expansion, or other useful metadatainformation.

The components and other features described herein may be implemented asdiscrete hardware components or integrated in the functionality ofhardware components such as ASICs, FPGAs, DSPs or similar devices. Inaddition, these components may be implemented as firmware or functionalcircuitry within hardware devices. Further, these components may beimplemented in any combination of hardware devices and softwarecomponents.

Some portions of the detailed descriptions are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

FIG. 4 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 400 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeembodiments, the machine may be connected (e.g., networked) to othermachines in a local area network (LAN), an intranet, an extranet, or theInternet. The machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a personal digital assistant (PDA), a cellular telephone, a webappliance, a server, a network router, switch or bridge, or any machinecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The exemplary computer system 400 includes a processing device 402, amain memory 404 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM) (such as synchronous DRAM (SDRAM) or RambusDRAM (RDRAM), etc.), a static memory 406 (e.g., flash memory, staticrandom access memory (SRAM), etc.), and a data storage device 418, whichcommunicate with each other via a bus 430.

Processing device 402 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device may be complex instruction setcomputing (CISC) microprocessor, reduced instruction set computer (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 402may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. Processing device 402 is configured to execute processinglogic 422 for determining public sentiment for performing the operationsand steps discussed herein.

Computer system 400 may further include a network interface device 408.Computer system 400 also may include a video display unit 410 (e.g., aliquid crystal display (LCD) or a cathode ray tube (CRT)), analphanumeric input device 412 (e.g., a keyboard), a cursor controldevice 414 (e.g., a mouse), and a signal generation device 416 (e.g., aspeaker).

Data storage device 418 may include a machine-readable storage medium(or more specifically a computer-readable storage medium) 420 having oneor more sets of instructions (e.g., processing logic 422) embodying anyone or more of the methodologies of functions described herein.Processing logic 422 may also reside, completely or at least partially,within main memory 404 and/or within processing device 402 duringexecution thereof by computer system 400; main memory 404 and processingdevice 402 also constituting machine-readable storage media. Contentprocessing logic 422 may further be transmitted or received over anetwork 426 via network interface device 408.

Machine-readable storage medium 420 may also be used to store the postand re-post analysis of the determining system 120. Whilemachine-readable storage medium 420 is shown in an exemplary embodimentto be a single medium, the term “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instruction forexecution by the machine and that causes the machine to perform any oneor more of the methodologies of the present disclosure. The term“machine-readable storage medium” shall accordingly be taken to include,but not be limited to, solid-state memories, and optical and magneticmedia.

The components and other features described herein may be implemented asdiscrete hardware components or integrated in the functionality ofhardware components such as ASICs, FPGAs, DSPs or similar devices. Inaddition, these components may be implemented as firmware or functionalcircuitry within hardware devices. Further, these components may beimplemented in any combination of hardware devices and softwarecomponents.

Some portions of the detailed descriptions are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as “identifying”, “determining”, “incrementing”,“associating”, “sending”, “classifying”, or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system's registersand memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

Embodiments of the present disclosure also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes or it may comprise a generalpurpose computer selectively activated or reconfigured by a computerprogram stored in the computer. Such a computer program may be stored ina computer readable storage medium, such as, but not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, flashmemory devices including universal serial bus (USB) storage devices(e.g., USB key devices) or any type of media suitable for storingelectronic instructions, each of which may be coupled to a computersystem bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will be apparent from the descriptionabove. In addition, the present disclosure is not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the disclosure as described herein.

Although specific embodiments have been illustrated and describedherein, those of ordinary skill in the art will appreciate that anarrangement configured to achieve the same results may be substitutedfor the specific embodiments shown. This description is intended tocover adaptations or variations of various embodiments of the presentdisclosure. It is to be understood that the above description has beenmade in an illustrative fashion, and not a restrictive one. Combinationsof the above embodiments, and other embodiments not specificallydescribed herein will be apparent to those of skill in the art uponreviewing the above description.

What is claimed is:
 1. A method comprising: identifying, by a processingdevice, a plurality of re-posts of a post, wherein each re-postcomprises a time stamp; determining, by the processing device, whetherthe time stamp of the re-post is within a first time interval or asecond time interval; incrementing, by the processing device, a firstcount associated with the first time interval when the time stamp iswithin the first time interval or a second count associated with thesecond time interval when the time stamp is within the second timeinterval; determining, by the processing device, a public sentimentattribute associated with the post in view of a comparison between thefirst count associated with the first time interval and the second countassociated with the second time interval; and transmitting, by theprocessing device, to a user interface of a computer processor, amessage comprising the public sentiment attribute to provide a user witha measure of an expansion rate of the post in view of the comparison. 2.The method of claim 1, wherein the first time interval and the secondtime interval are equal and consecutive time intervals.
 3. The method ofclaim 1, wherein the public sentiment attribute is a growing publicsentiment when the second count exceeds the first count.
 4. The methodof claim 1, wherein the public sentiment attribute is a growing publicsentiment when the second count exceeds the first count by apredetermined percentage.
 5. The method of claim 1, wherein the publicsentiment attribute is a high public sentiment when the first count isgreater than or equal to the second count and each of the first countand the second count exceeds a predetermined threshold.
 6. The method ofclaim 1, wherein a beginning time of the first time interval coincideswith a time stamp of the post.
 7. A system comprising: a memory; and aprocessing device communicably coupled to the memory, the processingdevice to: identify a plurality of re-posts of a post, wherein eachre-post comprises a time stamp; determine whether the time stamp of there-post is within a first time interval or a second time interval;increment a first count associated with the first time interval when thetime stamp is within the first time interval or a second countassociated with the second time interval when the time stamp is withinthe second time interval; determine a public sentiment attributeassociated with the post in view of a comparison between the first countassociated with the first time interval and the second count associatedwith the second time interval; and transmit to a user interface of acomputer processor a message comprising the public sentiment attributeto provide a user with a measure of an expansion rate of the post inview of the comparison.
 8. The system of claim 7, wherein the first timeinterval and the second time interval are equal and consecutive timeintervals.
 9. The system of claim 7, wherein the public sentimentattribute is a growing public sentiment when the second count exceedsthe first count.
 10. The system of claim 7, wherein the public sentimentattribute is a growing public sentiment when the second count exceedsthe first count by a predetermined percentage.
 11. The system of claim7, wherein the public sentiment attribute is a high public sentimentwhen the first count is greater than or equal to the second count andeach of the first count and the second count exceeds a predeterminedthreshold.
 12. The system of claim 7, wherein a beginning time of thefirst time interval coincides with a time stamp of the post.
 13. Anon-transitory computer readable storage device including instructionsthat, when executed by a processing device, cause the processing deviceto execute operations comprising: identifying, by a processing device, aplurality of re-posts of a post, wherein each re-post comprises a timestamp; determining, by the processing device, whether the time stamp ofthe re-post is within a first time interval or a second time interval;incrementing, by the processing device, a first count associated withthe first time interval when the time stamp is within the first timeinterval or a second count associated with the second time interval whenthe time stamp is within the second time interval; determining, by theprocessing device, a public sentiment attribute associated with the postin view of a comparison between the first count associated with thefirst time interval and the second count associated with the second timeinterval; and transmitting, by the processing device, to a userinterface of a computer processor, a message comprising the publicsentiment attribute to provide a user with a measure of an expansionrate of the post in view of the comparison.
 14. The non-transitorycomputer readable storage device of claim 13, wherein the first timeinterval and the second time interval are equal and consecutive timeintervals.
 15. The non-transitory computer readable storage device ofclaim 13, wherein the public sentiment attribute is a growing publicsentiment when the second count exceeds the first count.
 16. Thenon-transitory computer readable storage device of claim 13, wherein thepublic sentiment attribute is a growing public sentiment when the secondcount exceeds the first count by a predetermined percentage.
 17. Thenon-transitory computer readable storage device of claim 13, wherein thepublic sentiment attribute is a high public sentiment when the firstcount is greater than or equal to the second count and each of the firstcount and the second count exceeds a predetermined threshold.